Showing 4 results for Parametric Bootstrap
Nasrollah Iranpanah, H Tavasoli,
Volume 12, Issue 1 (11-2012)
Abstract
Mortality forecasts are nowadays widely used to create and modify retirement pension schemes, disability insurance systems and other social security programmers. Experience shows that static life tables overestimate death probabilities. The reason for this overestimation is that static life tables, through being computed for a specific period of time, cannot take into account the decreasing mortality trend over time. Dynamic life tables overcome this problem by incorporating the influence of the calendar when graduating mortality.
In this paper, we first apply the Lee-Carter model for estimation of mortality rate. Then, we use parametric and semi parametric bootstrap prediction intervals for mortality trend. Finally, these methods are applied for analysis of mortality data of Iran.
Mm Maghami, Nasrollah Iranpanah,
Volume 13, Issue 3 (11-2013)
Abstract
There are several methods for goodness of fit test for the skew normal distribution. This work focused on method of Meintanis [8] which is based on the empirical moment generating function. This test is discussed for the known and the unknown shape parameter. Meintanis [8] claimed that power of his test is higher than the Kolmogorov–Smirnov test. But this claim is true only for the known shape parameter. In this paper, we provide a new method for finding his test statistic that has more efficiency. Also Meintanis [8] not determine the size of himself test for the known shape parameter which in this paper we will determine it.
Nasrollah Iranpanah, Samaneh Noori Emamzadeh,
Volume 14, Issue 2 (7-2014)
Abstract
Traditional methods for testing equality of means are based on normality observations in each treatment, but parametric bootstrap methods offer a test statistic to estimate P-value by resampling. In article, first, Fisher, Cochran, Welch, James, Brown and Forsyth, Approximate F, Weerahandi, Adjust Welch and Parametric Bootstrap tests for testing hypothesis equality of means are defined. Then type one error and power of these tests were compared to each other by a simulation study for various sizes of samples and treatments. Finally sizes of these tests were calculated for the real data of Esfahan Cement factory.
Traditional methods for testing equality of means are based on normality observations in each treatment, but parametric bootstrap methods offer a test statistic to estimate P-value by resampling. In article, first, Fisher, Cochran, Welch, James, Brown and Forsyth, Approximate F, Weerahandi, Adjust Welch and Parametric Bootstrap tests for testing hypothesis equality of means are defined. Then type one error and power of these tests were compared to each other by a simulation study for various sizes of samples and treatments. Finally sizes of these tests were calculated for the real data of Esfahan Cement factory.
Nasrollah Iranpanah, Parisa Mikelani,
Volume 17, Issue 40 (9-2015)
Abstract
One of the main goals in studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by a autoregressive process. Then the bootstrap samples are generated by resampling from the residuals.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
One of the main goals in studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by a autoregressive process. Then the bootstrap samples are generated by resampling from the residuals.
In this paper, at first these sieve bootstrap methods are defined and then, in a simulation study sieve bootstrap prediction intervals are compared with a Standard Gaussian prediction interval. at last these methods are used to find the prediction intervals for weather data of Isfahan.
